Does COVID-19 Testing Create More Cases? An Empirical Evidence on the Importance of Mass Testing During a Pandemic

  1. Ali Ahmed

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Abstract

The importance of testing and surveillance of an infectious disease cannot be underestimated. The testing is the first step to detect an infectious disease, and mass testing can slow or mitigate the spread of an infectious disease. Despite overwhelming evidence and the importance of testing discussed in the literature, there have been claims that “more COVID-19 testing creates more cases”. Therefore, there is a need to study whether massive testing is the reason for detecting more positive COVID-19 cases. In this research, we used a dataset from the U.S. Department of Health & Human Services and empirically showed that by increasing the COVID-19 testing in the U.S., the spread of the COVID-19 decreased significantly. Our results indicate a negative relationship between the number of positive cases and the number of tests performed in the past months. The large-scale testing may have helped identify positive and asymptomatic cases early in the course of illness, which enabled individuals to isolate themselves, thus reducing the chances of spreading the diseases and slowing the spread of the pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.23.20248740: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.12.23.20248740v1 on medRxiv